Chapter 3 Notes: Model Training and Deployment

Overview

This chapter covers the design considerations for training ML models at scale and deploying them to production environments. It includes discussions on training infrastructure, model versioning, A/B testing, and deployment strategies.

Key Concepts

• Training Infrastructure: Distributed training, resource allocation, and optimization
• Model Versioning: Managing different model versions and experiments
• Deployment Strategies: Blue-green deployments, canary releases, and shadow deployments
• Serving Architecture: Online vs offline serving, latency requirements

Main Topics Covered

1. Distributed training systems
2. Model registry and versioning
3. Deployment patterns and strategies
4. Model serving architectures
5. Performance optimization
6. A/B testing for ML models

Design Considerations

• Latency vs throughput trade-offs
• Model size and memory constraints
• Scalability requirements
• Rollback strategies

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